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A Revised Molecular Model of Ovarian Cancer Biomarker CA125 (MUC16) Enabled by Long-read Sequencing
Open accessPublished version of a preprint
Abstract
The biomarker CA125, a peptide epitope located in several tandem repeats of the mucin MUC16, is the gold standard for monitoring regression and recurrence of high-grade serous ovarian cancer in response to therapy. However, the CA125 epitope along with several structural features of the MUC16 molecule are ill defined. One central aspect still unresolved is the number of tandem repeats in MUC16 and how many of these repeats contain the CA125 epitope. Studies from the early 2000s assembled short DNA reads to estimate that MUC16 contained 63 repeats. Here, we conduct Nanopore long-read sequencing of MUC16 transcripts from three primary ovarian tumors and established cell lines (OVCAR3, OVCAR5, and Kuramochi) for a more exhaustive and accurate estimation and sequencing of the MUC16 tandem repeats. The consensus sequence derived from these six sources was confirmed by proteomics validation and agrees with recent additions to the NCBI database. We propose a model of MUC16 containing 19—not 63—tandem repeats. In addition, we predict the structure of the tandem repeat domain using the deep learning algorithm, AlphaFold. The predicted structure displays an SEA domain and unstructured linker region rich in proline, serine, and threonine residues in all 19 tandem repeats. These studies now pave the way for a detailed characterization of the CA125 epitope. Sequencing and modeling of the MUC16 tandem repeats along with their glycoproteomic characterization, currently underway in our laboratories, will help identify novel epitopes in the MUC16 molecule that improve on the sensitivity and clinical utility of the current CA125 assay. Significance: Despite its crucial role in clinical management of ovarian cancer, the exact molecular sequence and structure of the biomarker, CA125, are not defined. Here, we combine long-read sequencing, mass spectrometry, and in silico modeling to provide the foundational dataset for a more complete characterization of the CA125 epitope.
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Metadata
- Authors
- Wang, C.-W., Weaver, S. D., Boonpattrawong, N., Schuster-Little, N., Patankar, M., & Whelan, R. J.
- Affiliations
- University of Kansas (US)
- University of Notre Dame (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Boonpattrawong, Nicha
- ORCID
- Venue
- Cancer Research Communications
- Published
- 2024-01-31
- Volume
- 4
- Issue
- 1
- Pages
- 253-263
- ISSN
- 2767-9764 (ISSN-L)
- PMC
- PMC10829539
- OpenAlex
- W4390703493
- Cited by
- 12
- References
- 51
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3
- FWCI
- 4.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancer Research Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- HHS | NIH | National Cancer Institute (R21CA267532)
- Tell Every Amazing Lady About Ovarian Cancer Louisa M. McGregor Ovarian Cancer Foundation (n/a)
- HHS | NIH | National Institute of General Medical Sciences (T32GM075762)
- Veterans Administration Medical Center (5I01BX005627)
- Diane Lindstrom Funds (n/a)
- Topics
- Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
- Physical Sciences › Chemistry › Organic Chemistry › Carbohydrate Chemistry and Synthesis
- Keywords
- CA125, ovarian cancer biomarker, tandem repeats, long-read sequencing, nanopore sequencing, AlphaFold structure prediction, SEA domain, mucin structure, high-grade serous ovarian cancer, glycoproteomics, OVCAR3
- MeSH terms
- Epitopes, Female, Humans, Membrane Proteins, Models, Molecular, Ovarian Neoplasms, Biomarkers, Tumor, Apoptosis, CA-125 Antigen, Cell Line, Tumor
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